论文信息 - High-fidelity fluorescence image restoration using deep unsupervised learning

High-fidelity fluorescence image restoration using deep unsupervised learning

Current deep learning methods for fluorescence image restoration necessitate numerous well-aligned image pairs for training. We develop an unsupervised learning framework for high-fidelity fluorescence image restoration without the laborious work of image annotation and registration.

[1] Loic A. Royer,et al. Content-aware image restoration: pushing the limits of fluorescence microscopy , 2018, Nature Methods.

[2] Jan Kautz,et al. Loss Functions for Image Restoration With Neural Networks , 2017, IEEE Transactions on Computational Imaging.

[3] William Graf,et al. Deep learning for cellular image analysis , 2019, Nature Methods.

[4] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.

[5] A. Ozcan,et al. Deep learning enables cross-modality super-resolution in fluorescence microscopy , 2018, Nature Methods.

[6] Loic A. Royer,et al. Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction , 2018, Nature Methods.

[7] Marco Stampanoni,et al. Unsupervised data to content transformation with histogram-matching cycle-consistent generative adversarial networks , 2019, Nature Machine Intelligence.

[8] Thomas Brox,et al. U-Net: deep learning for cell counting, detection, and morphometry , 2018, Nature Methods.